ACS Nano. 2022 Jul 22. doi: 10.1021/acsnano.2c02307. Online ahead of print.
ABSTRACT
Nanoparticles are an important class of materials that exhibit special properties arising from their high surface area-to-volume ratio. Scanning transmission electron microscopy (STEM) has played an important role in nanoparticle characterization, owing to its high spatial resolution, which allows direct visualization of composition and morphology with atomic precision. This typically comes at the cost of sample size, potentially limiting the accuracy and relevance of STEM results, as well as the ability to meaningfully track changes in properties that vary spatially. In this work, automated STEM data acquisition and analysis techniques are employed that enable physical and compositional properties of nanoparticles to be obtained at high resolution over length scales on the order of microns. This is demonstrated by studying the localized effects of potential cycling on electrocatalyst degradation across proton exchange membrane fuel cell cathodes. In contrast to conventional, manual STEM measurements, which produce particle size distributions representing hundreds of particles, these high-throughput automated methods capture tens of thousands of particles and enable nanoparticle size, number density, and composition to be measured as a function of position within the cathode. Comparing the properties of pristine and degraded fuel cells provides statistically robust evidence for the inhomogeneous nature of catalyst degradation across electrodes. These results demonstrate how high-throughput automated STEM techniques can be utilized to investigate local phenomena occurring in nanoparticle systems employed in practical devices.
PMID:35867353 | DOI:10.1021/acsnano.2c02307
